Assessment EDA
Conor Tompkins
1/26/2021
The purpose of this document is to perform EDA on the clean_assessments.csv data to identify patterns in the data that will inform a model of sale price.
library(tidyverse)
library(lubridate)
library(sf)
library(leaflet)
library(skimr)
library(hrbrthemes)
library(janitor)
library(scales)
library(here)
#here::i_am("scripts/eda")
options(scipen = 999, digits = 4)
theme_set(theme_ipsum())
assessment_data_path <- here("data/cleaned/big", "clean_assessment_data_geocoded.csv")
unified_geo_ids_path <- here("data/cleaned/big/unified_geo_ids/unified_geo_ids.shp")
assessments_valid <- read_csv(assessment_data_path) %>%
mutate(sale_month = factor(sale_month, levels = month.abb))
geo_ids <- st_read(unified_geo_ids_path, quiet = T)Get an idea of the data
glimpse(assessments_valid)## Rows: 157,925
## Columns: 31
## $ par_id <chr> "0467B00005000000", "0481L00052000000", "0936P003…
## $ usedesc <chr> "SINGLE FAMILY", "TOWNHOUSE", "SINGLE FAMILY", "S…
## $ muni_desc <chr> "Glassport", "South Fayette", "Bell Acres", "Eliz…
## $ sale_desc <chr> "Valid Sale", "Other Valid", "Other Valid", "Vali…
## $ sale_price <dbl> 61000, 96000, 688250, 66000, 48000, 192000, 90000…
## $ year_built <dbl> 1945, 1979, 1958, 1915, 1927, 1950, 1974, 1985, 1…
## $ style_desc <chr> "Old Style", "Townhouse", "Contemporary", "Bungal…
## $ bedrooms <dbl> 3, 2, 4, 2, 2, 3, 4, 3, 2, 3, 4, 3, 3, 5, 4, 4, 2…
## $ full_baths <dbl> 1, 1, 4, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 3, 2, 2, 1…
## $ half_baths <dbl> 0, 1, 2, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0…
## $ finished_living_area <dbl> 1026, 1388, 4958, 1388, 840, 1167, 2132, 1760, 91…
## $ lot_area <dbl> 4680, 1373, 68346, 29673, 6806, 11979, 11784, 600…
## $ grade_desc <chr> "Average", "Average", "Very Good", "Average", "Be…
## $ condition_desc <chr> "Average", "Average", "Average", "Average", "Aver…
## $ extfinish_desc <chr> "Brick", "Masonry Frame", "Brick", "Frame", "Fram…
## $ roof_desc <chr> "Shingle", "Shingle", "Metal", "Shingle", "Shingl…
## $ basement_desc <chr> "Full", "Full", "Full", "Full", "Full", "Full", "…
## $ cdu_desc <chr> "Average", "Good", "Average", "Average", "Average…
## $ heating_cooling_desc <chr> "Central Heat With AC", "Central Heat With AC", "…
## $ fireplaces <dbl> 0, 0, 2, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, NA, 1, …
## $ basement_garage <dbl> 0, 1, 2, 0, 0, 1, 2, 2, 0, 0, 0, 2, 1, 2, NA, 2, …
## $ sale_year <dbl> 2006, 2011, 2010, 1998, 2006, 2011, 1987, 1985, 2…
## $ sale_month <fct> Jun, Apr, Dec, Aug, Mar, Jun, Jul, Dec, May, Jan,…
## $ school_desc <chr> "South Allegheny", "South Fayette Twp", "Quaker V…
## $ house_age_at_sale <dbl> 61, 32, 52, 83, 79, 61, 13, 0, 29, 56, 70, 8, 37,…
## $ ac_flag <lgl> TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, …
## $ heat_type <chr> "Central Heat", "Central Heat", "Central Heat", "…
## $ sale_price_adj <dbl> 78314, 110456, 816887, 104789, 61624, 220912, 204…
## $ geo_id <chr> "South Allegheny", "South Fayette Township", "Qua…
## $ lng <dbl> -79.89, -80.12, -80.18, -79.87, -79.90, -79.88, -…
## $ lat <dbl> 40.33, 40.33, 40.58, 40.27, 40.38, 40.49, 40.54, …skim(assessments_valid)| Name | assessments_valid |
| Number of rows | 157925 |
| Number of columns | 31 |
| _______________________ | |
| Column type frequency: | |
| character | 15 |
| factor | 1 |
| logical | 1 |
| numeric | 14 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| par_id | 0 | 1 | 16 | 16 | 0 | 157924 | 0 |
| usedesc | 0 | 1 | 8 | 22 | 0 | 13 | 0 |
| muni_desc | 0 | 1 | 4 | 23 | 0 | 174 | 0 |
| sale_desc | 0 | 1 | 10 | 11 | 0 | 2 | 0 |
| style_desc | 0 | 1 | 3 | 13 | 0 | 20 | 0 |
| grade_desc | 0 | 1 | 4 | 13 | 0 | 7 | 0 |
| condition_desc | 22 | 1 | 4 | 9 | 0 | 8 | 0 |
| extfinish_desc | 1 | 1 | 3 | 14 | 0 | 8 | 0 |
| roof_desc | 207 | 1 | 4 | 7 | 0 | 6 | 0 |
| basement_desc | 30 | 1 | 4 | 10 | 0 | 5 | 0 |
| cdu_desc | 26 | 1 | 4 | 9 | 0 | 8 | 0 |
| heating_cooling_desc | 26 | 1 | 4 | 21 | 0 | 15 | 0 |
| school_desc | 0 | 1 | 6 | 20 | 0 | 78 | 0 |
| heat_type | 26 | 1 | 4 | 13 | 0 | 9 | 0 |
| geo_id | 0 | 1 | 7 | 23 | 0 | 54 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| sale_month | 0 | 1 | FALSE | 12 | Jun: 16781, Jul: 16754, Aug: 16503, May: 14878 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| ac_flag | 26 | 1 | 0.65 | TRU: 101971, FAL: 55928 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| sale_price | 0 | 1.00 | 130055.93 | 126771.46 | 325.00 | 55000.00 | 92500.00 | 160600.00 | 3070000.00 | ▇▁▁▁▁ |
| year_built | 0 | 1.00 | 1950.83 | 29.69 | 1800.00 | 1930.00 | 1953.00 | 1970.00 | 2020.00 | ▁▁▅▇▃ |
| bedrooms | 8 | 1.00 | 3.06 | 0.88 | 0.00 | 3.00 | 3.00 | 4.00 | 14.00 | ▂▇▁▁▁ |
| full_baths | 20 | 1.00 | 1.50 | 0.67 | 0.00 | 1.00 | 1.00 | 2.00 | 12.00 | ▇▁▁▁▁ |
| half_baths | 997 | 0.99 | 0.54 | 0.57 | 0.00 | 0.00 | 0.00 | 1.00 | 9.00 | ▇▁▁▁▁ |
| finished_living_area | 0 | 1.00 | 1703.14 | 761.18 | 0.00 | 1188.00 | 1512.00 | 2031.00 | 12790.00 | ▇▁▁▁▁ |
| lot_area | 0 | 1.00 | 13222.54 | 37790.03 | 0.00 | 3980.00 | 7521.00 | 12981.00 | 4859466.00 | ▇▁▁▁▁ |
| fireplaces | 9040 | 0.94 | 0.43 | 0.57 | 0.00 | 0.00 | 0.00 | 1.00 | 7.00 | ▇▁▁▁▁ |
| basement_garage | 4046 | 0.97 | 0.79 | 0.84 | 0.00 | 0.00 | 1.00 | 1.00 | 6.00 | ▇▂▁▁▁ |
| sale_year | 0 | 1.00 | 2002.01 | 11.96 | 1976.00 | 1993.00 | 2003.00 | 2013.00 | 2021.00 | ▃▆▇▇▇ |
| house_age_at_sale | 0 | 1.00 | 51.19 | 30.52 | 0.00 | 28.00 | 50.00 | 71.00 | 219.00 | ▇▇▂▁▁ |
| sale_price_adj | 0 | 1.00 | 179798.20 | 149366.07 | 417.25 | 91448.57 | 142828.85 | 219234.73 | 3507768.06 | ▇▁▁▁▁ |
| lng | 0 | 1.00 | -79.97 | 0.12 | -80.35 | -80.05 | -79.98 | -79.88 | -79.70 | ▁▂▇▆▂ |
| lat | 0 | 1.00 | 40.45 | 0.09 | 40.20 | 40.38 | 40.44 | 40.51 | 40.67 | ▁▅▇▅▂ |
Sales Price
Inflation-adjusted sales price (sale_price_adj) is normally distributed on the log10() scale, as expected.
assessments_valid %>%
ggplot(aes(sale_price_adj)) +
geom_density() +
scale_x_log10(labels = dollar) +
labs(x = "Inflation-adjusted sale price log10 scale")
Adjusting for inflation (sale_price_adj is in 2020 dollars) removes a lot of the drift over time.
assessments_valid %>%
select(sale_year, sale_price, sale_price_adj) %>%
filter(sale_year < 2021) %>%
pivot_longer(cols = contains("sale_price")) %>%
mutate(name = case_when(name == "sale_price" ~ "Nominal dollars",
name == "sale_price_adj" ~ "Inflation-adjusted 2020 dollars")) %>%
group_by(sale_year, name) %>%
summarize(median_price = median(value)) %>%
ggplot(aes(sale_year, median_price, color = name)) +
geom_line() +
scale_y_continuous(labels = dollar) +
labs(x = "Sale Year",
y = "Median Price",
color = NULL)
House sales have plateaued since the 2008 mortgage crisis, with more year-to-year variation.
sale_year_count <- assessments_valid %>%
count(sale_year)
sale_year_count %>%
mutate(year_flag = case_when(sale_year == 2021 ~ "2021",
sale_year < 2021 ~ "Before 2021")) %>%
ggplot(aes(sale_year, n)) +
geom_smooth(data = sale_year_count %>%
filter(sale_year != 2021),
se = F) +
geom_point(aes(color = year_flag)) +
labs(x = "Sale Year",
y = "Sales",
color = NULL)
Median sale price and the number of sales both peak over the summer months.
assessments_valid %>%
mutate(sale_month = fct_rev(sale_month)) %>%
add_count(sale_month) %>%
ggplot(aes(sale_month, sale_price_adj, group = sale_month, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
labs(fill = "Sales") +
scale_y_continuous(labels = label_dollar()) +
coord_cartesian(ylim = c(0, 500000)) +
scale_fill_viridis_c() +
labs(y = "Inflation-adjusted sale price")
Location
The location of the house (geo_id) has a big impact on sale_price_adj (interactive map).
geo_id_median_price <- assessments_valid %>%
group_by(geo_id) %>%
summarize(median_price = median(sale_price_adj))
pal <- colorNumeric(
palette = "viridis",
domain = geo_id_median_price$median_price)
geo_ids %>%
left_join(geo_id_median_price) %>%
leaflet() %>%
addProviderTiles(providers$Stamen.TonerLite,
options = providerTileOptions(noWrap = TRUE,
minZoom = 9,
#maxZoom = 8
)) %>%
addPolygons(popup = ~ str_c(geo_id, " ", "median price: ", dollar(median_price), sep = ""),
fillColor = ~pal(median_price),
fillOpacity = .7,
color = "black",
weight = 3) %>%
addLegend("bottomright", pal = pal, values = ~median_price,
title = "Median sale price",
labFormat = labelFormat(prefix = "$"),
opacity = 1)assessments_valid %>%
add_count(geo_id) %>%
mutate(geo_id = fct_reorder(geo_id, sale_price_adj, .fun = median)) %>%
ggplot(aes(sale_price_adj, geo_id, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_continuous(label = dollar) +
scale_fill_viridis_c() +
coord_cartesian(xlim = c(0, 500000)) +
labs(fill = "Sales",
x = "Inflation-adjusted sale price")
House Characteristics
The age of the house at time of sale has a negative relationship with sale price, but is heteroskedastic. This indicates that some houses retain their value better over time (and/or houses that don’t retain value aren’t bought and sold).
assessments_valid %>%
ggplot(aes(house_age_at_sale, sale_price_adj)) +
geom_density_2d_filled(contour_var = "count") +
scale_y_log10(labels = dollar) +
guides(fill = guide_colorsteps()) +
labs(y = "Inflation-adjusted sale price log10 scale",
fill = "Sales")
Lot Area
There is a positive relationship between lot_area and sale_price_adj.
assessments_valid %>%
ggplot(aes(lot_area, sale_price_adj)) +
geom_density_2d_filled(contour_var = "count") +
scale_y_log10(labels = dollar) +
scale_x_log10() +
guides(fill = guide_colorsteps()) +
labs(x = "Lot area sq. ft. log10 scale",
y = "Inflation-adjusted sale price log10 scale",
fill = "Sales")
lot_area varies drastically across geo_id, so there is probably a useful feature to be engineered.
assessments_valid %>%
add_count(geo_id) %>%
mutate(geo_id = fct_reorder(geo_id, lot_area, .fun = median)) %>%
ggplot(aes(lot_area, geo_id, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
#scale_x_log10() +
scale_fill_viridis_c() +
coord_cartesian(xlim = c(0, 50000)) +
labs(x = "Lot Area sq. ft",
fill = "Sales")
lot_area also varies across style_desc.
assessments_valid %>%
add_count(style_desc) %>%
mutate(style_desc = fct_reorder(style_desc, lot_area, .fun = median)) %>%
ggplot(aes(lot_area, style_desc, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
#scale_x_log10() +
scale_fill_viridis_c() +
coord_cartesian(xlim = c(0, 200000)) +
labs(x = "Lot Area sq. ft.",
fill = "Sales")
Finished Living Area
finished_living_area will be a useful variable.
assessments_valid %>%
ggplot(aes(finished_living_area, sale_price_adj)) +
geom_density_2d_filled(contour_var = "count") +
scale_x_log10() +
scale_y_log10(labels = dollar) +
guides(fill = guide_colorsteps()) +
labs(x = "Finished Living Area sq. ft. log10 scale",
y = "Inflation-adjusted sale price log10 scale",
fill = "Sales")
finished_living_area also varies across style_desc.
assessments_valid %>%
add_count(style_desc) %>%
mutate(style_desc = fct_reorder(style_desc, finished_living_area, .fun = median)) %>%
ggplot(aes(finished_living_area, style_desc, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_log10() +
scale_fill_viridis_c() +
labs(x = "Finished Living Area sq. ft. log10 scale",
fill = "Sales")
finished_living_area also varies across geo_id, but not as drastically.
assessments_valid %>%
add_count(geo_id) %>%
mutate(geo_id = fct_reorder(geo_id, finished_living_area, .fun = median)) %>%
ggplot(aes(finished_living_area, geo_id, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_log10() +
#coord_cartesian(xlim = c(0, 10000)) +
scale_fill_viridis_c() +
labs(x = "Finished Living Area sq. ft. log10 scale",
fill = "Sales")
Grade
grade_desc has a clear relationship with sale_price_adj.
assessments_valid %>%
mutate(grade_desc = fct_reorder(grade_desc, sale_price_adj, median)) %>%
add_count(grade_desc) %>%
ggplot(aes(sale_price_adj, grade_desc, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_log10(labels = dollar) +
scale_fill_viridis_c() +
labs(x = "Inflation-adjusted price log10 scale",
fill = "Sales")
Condition
condition_desc also has a relationship, but some of the levels can probably be collapsed.
assessments_valid %>%
mutate(condition_desc = fct_reorder(condition_desc, sale_price_adj, median)) %>%
add_count(condition_desc) %>%
ggplot(aes(sale_price_adj, condition_desc, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_log10(labels = dollar) +
scale_fill_viridis_c() +
labs(x = "Inflation-adjusted price log10 scale",
fill = "Sales")
There are 4 main types of houses, with a lot of low-n types that can be collapsed.
assessments_valid %>%
count(style_desc) %>%
mutate(style_desc = fct_reorder(style_desc, n, .fun = max)) %>%
ggplot(aes(n, style_desc)) +
geom_col()
Most houses that have been sold were built between 1940 and 1980.
assessments_valid %>%
count(year_built) %>%
ggplot(aes(year_built, n)) +
geom_point()
There is some time-series pattern in when different types of houses were created.
assessments_valid %>%
add_count(style_desc) %>%
filter(n > 5000) %>%
mutate(style_desc = fct_reorder(style_desc, n, .fun = "max", .desc = T)) %>%
ggplot(aes(year_built, fill = style_desc)) +
geom_histogram(binwdidth = 30) +
guides(fill = "none") +
facet_wrap(~style_desc, scales = "free_y", ncol = 2)
Bathrooms
Most homes have between 1 and 2 full and half bathrooms.
assessments_valid %>%
count(full_baths, half_baths) %>%
complete(full_baths = 0:12, half_baths = 0:9, fill = list(n = 0)) %>%
ggplot(aes(full_baths, half_baths, fill = n)) +
geom_tile() +
scale_fill_viridis_c() +
scale_x_continuous(breaks = c(0:12),
expand = c(0,0)) +
scale_y_continuous(breaks = c(0:12),
expand = c(0,0)) +
coord_equal() +
labs(fill = "Sales") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
fullbaths and sale_price_adj are positively related.
assessments_valid %>%
add_count(full_baths) %>%
ggplot(aes(sale_price_adj, full_baths, fill = n, group = full_baths)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_log10(label = dollar) +
scale_y_continuous(breaks = c(0:12)) +
scale_fill_viridis_c() +
labs(x = "Inflation-adjusted price log10 scale",
fill = "Sales")
There appear to be diminishing returns on the number of half bathrooms.
assessments_valid %>%
add_count(half_baths) %>%
ggplot(aes(sale_price_adj, half_baths, fill = n, group = half_baths)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_log10(label = dollar) +
scale_y_continuous(breaks = c(0:9)) +
scale_fill_viridis_c() +
labs(x = "Inflation-adjusted price log10 scale",
fill = "Sales")
Heating and cooling
Need to split these out to heat_type and ac_flag.
assessments_valid %>%
mutate(heating_cooling_desc = fct_explicit_na(heating_cooling_desc),
heating_cooling_desc = fct_reorder(heating_cooling_desc, sale_price_adj, .fun = median)) %>%
add_count(heating_cooling_desc) %>%
ggplot(aes(sale_price_adj, heating_cooling_desc, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_log10(label = dollar) +
scale_fill_viridis_c() +
labs(x = "Inflation-adjusted price log10 scale",
fill = "Sales")
The type of heating is positively related to sale price.
assessments_valid %>%
mutate(heat_type = fct_explicit_na(heat_type),
heat_type = fct_reorder(heat_type, sale_price_adj, .fun = median)) %>%
add_count(heat_type) %>%
ggplot(aes(sale_price_adj, heat_type, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_log10(label = dollar) +
scale_fill_viridis_c() +
labs(x = "Inflation-adjusted price log10 scale",
fill = "Sales")
Whether the house has AC is also positively related to sale price.
assessments_valid %>%
mutate(ac_flag = as.character(ac_flag),
ac_flag = fct_explicit_na(ac_flag)) %>%
mutate(ac_flag = fct_reorder(ac_flag, sale_price_adj, .fun = median)) %>%
add_count(ac_flag) %>%
ggplot(aes(sale_price_adj, ac_flag, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_log10(label = dollar) +
scale_fill_viridis_c() +
labs(fill = "Sales")
Exterior
Impute missing based on mode for geo_id and style_desc.
assessments_valid %>%
mutate(extfinish_desc = fct_explicit_na(extfinish_desc),
extfinish_desc = fct_reorder(extfinish_desc, sale_price_adj, .fun = median)) %>%
add_count(extfinish_desc) %>%
ggplot(aes(sale_price_adj, extfinish_desc, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_log10(label = dollar) +
scale_fill_viridis_c() +
labs(x = "Inflation-adjusted price log10 scale",
fill = "Sales")
Roof
Impute missing based on mode for geo_id and style_desc.
assessments_valid %>%
mutate(roof_desc = fct_explicit_na(roof_desc),
roof_desc = fct_reorder(roof_desc, sale_price_adj, .fun = median)) %>%
add_count(roof_desc) %>%
ggplot(aes(sale_price_adj, roof_desc, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_log10(label = dollar) +
scale_fill_viridis_c() +
labs(x = "Inflation-adjusted price log10 scale",
fill = "Sales")
Basement
Impute missing based on mode for geo_id and style_desc.
assessments_valid %>%
mutate(basement_desc = fct_explicit_na(basement_desc),
basement_desc = fct_reorder(basement_desc, sale_price_adj, .fun = median)) %>%
add_count(basement_desc) %>%
ggplot(aes(sale_price_adj, basement_desc, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_log10(label = dollar) +
scale_fill_viridis_c() +
labs(fill = "Sales")
Basement garage
Impute missing based on mode for geo_id and style_desc. Need to look up what this column is. Number of cars that can fit in the basement garage?
max_basement_garage <- assessments_valid %>%
summarize(max(basement_garage, na.rm = T)) %>%
pull()
assessments_valid %>%
select(sale_price_adj, basement_garage) %>%
mutate(basement_garage = as.character(basement_garage),
basement_garage = fct_explicit_na(basement_garage),
basement_garage = fct_relevel(basement_garage, c(as.character(0:max_basement_garage), "(Missing)"))) %>%
add_count(basement_garage) %>%
ggplot(aes(sale_price_adj, basement_garage, group = basement_garage, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_log10(label = dollar) +
scale_fill_viridis_c() +
labs(fill = "Sales")
Fireplaces
Positive relationship between number of fireplaces and sale price, but most houses have 1 or 2. Consider changing to lgl fireplace_flag column where it checks fireplaces >= 1.
#need to deal with missing fireplace rows
max_fireplaces <- assessments_valid %>%
summarize(max(fireplaces, na.rm = T)) %>%
pull()
assessments_valid %>%
select(sale_price_adj, fireplaces) %>%
mutate(fireplaces = as.character(fireplaces),
fireplaces = fct_explicit_na(fireplaces),
fireplaces = fct_relevel(fireplaces, c(as.character(0:max_fireplaces), "(Missing)"))) %>%
add_count(fireplaces) %>%
ggplot(aes(sale_price_adj, fireplaces, group = fireplaces, fill = n)) +
geom_boxplot(outlier.alpha = 0,
color = "grey") +
scale_x_log10(label = dollar) +
scale_fill_viridis_c() +
labs(x = "Inflation-adjusted price log10 scale",
fill = "Sales")
Identify UI cutoffs
assessments_valid %>%
ggplot(aes(lot_area)) +
geom_density() +
geom_vline(xintercept = 40000) +
scale_x_log10()
assessments_valid %>%
ggplot(aes(finished_living_area)) +
geom_density() +
scale_x_log10() +
geom_vline(xintercept = 10000)